• Dellaquila, Daniele
• Barone, Francesco Pio
• Russo, Marco

Abstract

<jats:title>Abstract</jats:title><jats:p>Advanced LIGO and Advanced Virgo ground-based interferometers are instruments capable to detect gravitational wave (GW) signals exploiting advanced laser interferometry techniques. The underlying data analysis task consists in identifying specific patterns in noisy timeseries, but it is made extremely complex by the incredibly small amplitude of the target signals. In this scenario, the development of effective GW detection algorithms is crucial. We propose a novel layered framework for real-time detection of GWs inspired by speech processing techniques and, in the present implementation, based on a state-of-the-art machine learning approach involving a hybridization of genetic programming and neural networks. The key aspects of the newly proposed framework are: the well structured, layered approach, and the low computational complexity. The paper describes the basic concepts of the framework and the derivation of the first three layers. Even if, in the present implementation, the layers are based on models derived using a machine learning approach, the proposed layered structure has a universal nature. Compared to more complex approaches, such as convolutional neural networks, which comprise a parameter set of several tens of MB and were tested exclusively for fixed length data samples, our framework has lower accuracy (e.g. it identifies <jats:inline-formula><jats:tex-math><?CDATA $45\%$?></jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"><mml:mn>45</mml:mn><mml:mi mathvariant="normal">%</mml:mi></mml:math><jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="mlstad1200ieqn1.gif" xlink:type="simple" /></jats:inline-formula> of low signal-to-noise-ration GW signals, against <jats:inline-formula><jats:tex-math><?CDATA $65\%$?></jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"><mml:mn>65</mml:mn><mml:mi mathvariant="normal">%</mml:mi></mml:math><jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="mlstad1200ieqn2.gif" xlink:type="simple" /></jats:inline-formula> of the state-of-the-art, at a false alarm probability of 10<jats:sup>−2</jats:sup>), but has a much lower computational complexity (it exploits only 4 numerical features in the present implementation) and a higher degree of modularity. Furthermore, the exploitation of short-term features makes the results of the new framework virtually independent against time-position of GW signals, simplifying its future exploitation in real-time multi-layer pipelines for gravitational-wave detection with new generation interferometers.</jats:p>

Topics

• impedance spectroscopy
• layered
• machine learning
• interferometry